Papers with dialogue learning
Self-Supervised Dialogue Learning (P19-1)
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| Challenge: | Existing dialogue systems have failed to capture the order of utterances in coherent dialogues. |
| Approach: | They propose a self-supervised learning task to capture the flow of dialogues . they propose 'inconsistent order detection' task to predict whether utterance is ordered or misordered . |
| Outcome: | The proposed methods can be applied to open-domain and task-oriented dialogue scenarios and achieve state-of-the-art performance on the OpenSubtitiles and Movie-Ticket Booking datasets. |
Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweight (2020.acl-main)
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| Challenge: | Current state-of-the-art neural dialogue models learn from human conversations . however, due to the open-ended nature of human conversations, the quality of training data varies . |
| Approach: | They propose a data manipulation framework to augment and highlight effective training samples . they also propose to increase its manipulation skills through gradient descent with validation samples a reshaping framework to proactively restructure the data distribution towards reliable samples is also proposed . |
| Outcome: | The proposed framework improves the performance of open-domain neural dialogue models with respect to evaluation metrics and human judgments. |
OmniFlatten: An End-to-end GPT Model for Seamless Voice Conversation (2025.acl-long)
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Qinglin Zhang, Luyao Cheng, Chong Deng, Qian Chen, Wen Wang, Siqi Zheng, Jiaqing Liu, Hai Yu, Chao-Hong Tan, Zhihao Du, ShiLiang Zhang
| Challenge: | Full-duplex spoken dialogue systems allow simultaneous bidirectional communication . low latency and natural interactions in full-duplice systems remains a challenge . |
| Approach: | They propose a multi-stage post-training scheme that adapts a text large language model into a speech-text dialogue LLM. |
| Outcome: | The proposed model can model human conversation behaviors with low latency and natural interactions with low delay. |